Papers with JFLEG
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches for grammatical error correction (GEC) rely on supervised learning with manually created datasets. |
| Approach: | They propose to denoise GEC datasets by leveraging prediction consistency of existing models. |
| Outcome: | The proposed method outperforms baseline methods on CoNLL-2014, JFLEG, and BEA-2019 benchmarks. |
Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task (N18-1)
Copied to clipboard
| Challenge: | Previously, neural methods in grammatical error correction did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) systems that improve on results by SMT use their set-up as a backbone for more complex systems. |
| Approach: | They propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. |
| Outcome: | The proposed methods outperform state-of-the-art neural GEC systems by 10% M2 on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. |
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough? (N19-1)
Copied to clipboard
| Challenge: | Existing studies have evaluated grammatical error correction models on a single corpus, but the evaluation is incomplete because the task difficulty varies depending on the corpus and conditions such as proficiency levels of the writers and essay topics. |
| Approach: | They evaluate the performance of several GEC models against various learner corpora and compare their rankings against the corpus. |
| Outcome: | The evaluation of several models against learner corpora shows that the models’ rankings vary depending on the corpus, indicating that single-corpus evaluation is insufficient for GEC models. |
Neural Machine Translation of Text from Non-Native Speakers (N19-1)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data. |
| Approach: | They propose to augment training data with sentences containing artificially-introduced grammatical errors to make the system more robust to such errors. |
| Outcome: | The proposed approach recovers 1.0 BLEU out of 2.4 BLUE lost due to grammatical errors on a set of Spanish translations of the JFLEG grammar error correction corpus. |
Corpora Generation for Grammatical Error Correction (N19-1)
Copied to clipboard
| Challenge: | Grammatical Error Correction (GEC) is a computational task that requires large amounts of data to solve. |
| Approach: | They propose two approaches to generate large parallel datasets for GEC using publicly available Wikipedia edit histories using minimal filtration heuristics and round-trip translation through bridge languages. |
| Outcome: | The proposed methods yield similar sized parallel corpora with around 4B tokens and are far ahead of the state-of-the-art on the CoNLL ‘14 benchmark and the JFLEG task. |